Data Analytics

Thanks for the amazing response on last poll. Because of the huge request, I have decided to post important data analyst questions in the channel on daily basis 😊 Data Analyst Interview Part-1 1. What is the difference between a primary key and a foreign key in SQL? Answer: A primary key uniquely identifies each record in a table. It must be unique and cannot contain NULL values. A foreign key is a field in one table that refers to the primary key in another table, establishing a relationship between the two tables. Example:

CREATE TABLE Customers ( CustomerID INT PRIMARY KEY, Name VARCHAR(50) ); CREATE TABLE Orders ( OrderID INT PRIMARY KEY, CustomerID INT, FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID) );

2. What are the different types of JOINs in SQL? Answer: SQL supports different types of JOINs to combine data from multiple tables: INNER JOIN: Returns only matching records in both tables. LEFT JOIN: Returns all records from the left table and matching records from the right table. RIGHT JOIN: Returns all records from the right table and matching records from the left table. FULL OUTER JOIN: Returns all records from both tables, with NULLs where there is no match. SELF JOIN: A table is joined with itself. CROSS JOIN: Produces a Cartesian product of both tables. Example:

SELECT Customers.Name, Orders.OrderID FROM Customers INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID; 

3. What are Pivot Tables in Excel, and why are they used? Answer: A Pivot Table in Excel allows users to summarize, analyze, explore, and present data dynamically. It helps in: Summarizing large datasets quickly. Performing calculations like sum, count, average, etc. Creating reports without using complex formulas. Example Use Case: If you have sales data with columns for region, product, and revenue, a pivot table can show total revenue by region and product category. Steps to create a Pivot Table: Select your dataset. Go to Insert → Pivot Table. Choose where to place the Pivot Table. Drag fields into Rows, Columns, Values, and Filters. 4. Explain the difference between COUNT(), COUNT(*), and COUNT(column_name) in SQL. Answer: COUNT(): Returns the number of rows where a column is NOT NULL. COUNT(*): Returns the total number of rows in a table, including NULL values. COUNT(column_name): Counts non-NULL values in a specific column. Example:

SELECT COUNT(*) FROM Orders; -- Counts all rows SELECT COUNT(CustomerID) FROM Orders; -- Counts only non-NULL CustomerIDs 

5. How do you handle missing values in Python using Pandas? Answer: Missing values can be handled using Pandas functions: Drop missing values: df.dropna() Fill missing values: df.fillna(value) Replace missing values: df.replace(to_replace, value) Check for missing values: df.isnull().sum() Example:

import pandas as pd df = pd.DataFrame({'A': [1, 2, None, 4], 'B': [None, 2, 3, 4]}) df.fillna(0, inplace=True) # Replace NaN with 0

I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://t.me/DataSimplifier Like this post for if you want me to continue the interview series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

Day 21: Review Week 3 Topics & Complex SQL Challenges 📌 Topics to Review from Week 3 Window Functions – (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG). Stored Procedures – Creating, executing, and using parameters. Triggers – AFTER and INSTEAD OF triggers. Views – Creating, modifying, and using indexed views. Transactions & ACID Properties – Ensuring data consistency. 📝 Complex SQL Challenges 1️⃣ Challenge: Find the Second Highest Salary (Without Using LIMIT or TOP) You have an Employees table. Write a query to find the second highest salary.

SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees); 

2️⃣ Challenge: Get Consecutive Login Streaks Given a Logins table with UserID and LoginDate, find users who logged in for three consecutive days.

SELECT DISTINCT L1.UserID FROM Logins L1 JOIN Logins L2 ON L1.UserID = L2.UserID AND L1.LoginDate = L2.LoginDate - 1 JOIN Logins L3 ON L1.UserID = L3.UserID AND L1.LoginDate = L3.LoginDate - 2; 

3️⃣ Challenge: Rank Employees by Salary Within Each Department

SELECT EmployeeID, Name, Department, Salary, RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS SalaryRank FROM Employees; 

✅ Action Plan for Today Review Week 3 Topics – Revisit notes, practice stored procedures, and triggers. Solve These Complex Challenges – Try modifying them for different cases. Ask Yourself: What happens if two employees have the same second-highest salary? How would you handle ties in ranking employees? Can you optimize these queries for better performance? 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Python Learning Plan in 2025 |-- Week 1: Introduction to Python | |-- Python Basics | | |-- What is Python? | | |-- Installing Python | | |-- Introduction to IDEs (Jupyter, VS Code) | |-- Setting up Python Environment | | |-- Anaconda Setup | | |-- Virtual Environments | | |-- Basic Syntax and Data Types | |-- First Python Program | | |-- Writing and Running Python Scripts | | |-- Basic Input/Output | | |-- Simple Calculations | |-- Week 2: Core Python Concepts | |-- Control Structures | | |-- Conditional Statements (if, elif, else) | | |-- Loops (for, while) | | |-- Comprehensions | |-- Functions | | |-- Defining Functions | | |-- Function Arguments and Return Values | | |-- Lambda Functions | |-- Modules and Packages | | |-- Importing Modules | | |-- Standard Library Overview | | |-- Creating and Using Packages | |-- Week 3: Advanced Python Concepts | |-- Data Structures | | |-- Lists, Tuples, and Sets | | |-- Dictionaries | | |-- Collections Module | |-- File Handling | | |-- Reading and Writing Files | | |-- Working with CSV and JSON | | |-- Context Managers | |-- Error Handling | | |-- Exceptions | | |-- Try, Except, Finally | | |-- Custom Exceptions | |-- Week 4: Object-Oriented Programming | |-- OOP Basics | | |-- Classes and Objects | | |-- Attributes and Methods | | |-- Inheritance | |-- Advanced OOP | | |-- Polymorphism | | |-- Encapsulation | | |-- Magic Methods and Operator Overloading | |-- Design Patterns | | |-- Singleton | | |-- Factory | | |-- Observer | |-- Week 5: Python for Data Analysis | |-- NumPy | | |-- Arrays and Vectorization | | |-- Indexing and Slicing | | |-- Mathematical Operations | |-- Pandas | | |-- DataFrames and Series | | |-- Data Cleaning and Manipulation | | |-- Merging and Joining Data | |-- Matplotlib and Seaborn | | |-- Basic Plotting | | |-- Advanced Visualizations | | |-- Customizing Plots | |-- Week 6-8: Specialized Python Libraries | |-- Web Development | | |-- Flask Basics | | |-- Django Basics | |-- Data Science and Machine Learning | | |-- Scikit-Learn | | |-- TensorFlow and Keras | |-- Automation and Scripting | | |-- Automating Tasks with Python | | |-- Web Scraping with BeautifulSoup and Scrapy | |-- APIs and RESTful Services | | |-- Working with REST APIs | | |-- Building APIs with Flask/Django | |-- Week 9-11: Real-world Applications and Projects | |-- Capstone Project | | |-- Project Planning | | |-- Data Collection and Preparation | | |-- Building and Optimizing Models | | |-- Creating and Publishing Reports | |-- Case Studies | | |-- Business Use Cases | | |-- Industry-specific Solutions | |-- Integration with Other Tools | | |-- Python and SQL | | |-- Python and Excel | | |-- Python and Power BI | |-- Week 12: Post-Project Learning | |-- Python for Automation | | |-- Automating Daily Tasks | | |-- Scripting with Python | |-- Advanced Python Topics | | |-- Asyncio and Concurrency | | |-- Advanced Data Structures | |-- Continuing Education | | |-- Advanced Python Techniques | | |-- Community and Forums | | |-- Keeping Up with Updates | |-- Resources and Community | |-- Online Courses (Coursera, edX, Udemy) | |-- Books (Automate the Boring Stuff, Python Crash Course) | |-- Python Blogs and Podcasts | |-- GitHub Repositories | |-- Python Communities (Reddit, Stack Overflow) Here you can find essential Python Interview Resources👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like this post for more resources like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

Day 20: Triggers 1. What is a Trigger? A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE. 2. Types of Triggers: AFTER Trigger (a.k.a. FOR Trigger) – Executes after the triggering event. INSTEAD OF Trigger – Replaces the default action of an INSERT, UPDATE, or DELETE. 3. AFTER Trigger Example: Triggers after a row is inserted into the Employees table.

CREATE TRIGGER trg_AfterInsert ON Employees AFTER INSERT AS BEGIN PRINT 'A new employee record has been inserted!'; END; 
Test the trigger:
INSERT INTO Employees (EmployeeID, Name, Department) VALUES (101, 'John Doe', 'IT'); 

After execution, the message "A new employee record has been inserted!" appears. 4. INSTEAD OF Trigger Example Prevents deleting employees from the Employees table but logs the request.

CREATE TRIGGER trg_InsteadOfDelete ON Employees INSTEAD OF DELETE AS BEGIN PRINT 'Delete operation blocked. Logging attempt...'; INSERT INTO DeleteLogs (EmployeeID, DeleteTime) SELECT EmployeeID, GETDATE() FROM deleted; END; 
Test the trigger:
DELETE FROM Employees WHERE EmployeeID = 101; 
Instead of deleting, it logs the deletion attempt.

5. Viewing & Dropping Triggers List triggers on a table:

SELECT name FROM sys.triggers WHERE parent_id = OBJECT_ID('Employees'); 
Drop a trigger:
DROP TRIGGER trg_AfterInsert; 

6. Best Practices for Triggers: ✅ Keep triggers lightweight to avoid performance issues. ✅ Use triggers only when necessary (consider stored procedures for flexibility). ✅ Avoid recursive triggers (where a trigger fires another trigger). ✅ Log actions to track unwanted modifications. Action Steps: Create an AFTER INSERT trigger to log new entries into an audit table. Create an INSTEAD OF UPDATE trigger to prevent salary updates above a certain limit. Experiment with retrieving deleted records using the deleted table inside a trigger. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

Day 20: Triggers 1. What is a Trigger? A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE. 2. Types of Triggers: AFTER Trigger (a.k.a. FOR Trigger) – Executes after the triggering event. INSTEAD OF Trigger – Replaces the default action of an INSERT, UPDATE, or DELETE. 3. AFTER Trigger Example: Triggers after a row is inserted into the Employees table.

CREATE TRIGGER trg_AfterInsert ON Employees AFTER INSERT AS BEGIN PRINT 'A new employee record has been inserted!'; END; 
Test the trigger:
INSERT INTO Employees (EmployeeID, Name, Department) VALUES (101, 'John Doe', 'IT'); 

After execution, the message "A new employee record has been inserted!" appears. 4. INSTEAD OF Trigger Example Prevents deleting employees from the Employees table but logs the request.

CREATE TRIGGER trg_InsteadOfDelete ON Employees INSTEAD OF DELETE AS BEGIN PRINT 'Delete operation blocked. Logging attempt...'; INSERT INTO DeleteLogs (EmployeeID, DeleteTime) SELECT EmployeeID, GETDATE() FROM deleted; END; 
Test the trigger:
DELETE FROM Employees WHERE EmployeeID = 101; 
Instead of deleting, it logs the deletion attempt.

5. Viewing & Dropping Triggers List triggers on a table:

SELECT name FROM sys.triggers WHERE parent_id = OBJECT_ID('Employees'); 
Drop a trigger:
DROP TRIGGER trg_AfterInsert; 

6. Best Practices for Triggers: ✅ Keep triggers lightweight to avoid performance issues. ✅ Use triggers only when necessary (consider stored procedures for flexibility). ✅ Avoid recursive triggers (where a trigger fires another trigger). ✅ Log actions to track unwanted modifications. Action Steps: Create an AFTER INSERT trigger to log new entries into an audit table. Create an INSTEAD OF UPDATE trigger to prevent salary updates above a certain limit. Experiment with retrieving deleted records using the deleted table inside a trigger. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Certificates have their own value in proving your skills, but completing a course just for the sake of a certificate won’t help you at all. What truly matters is how well you understand and apply what you’ve learned. Whatever course you take, focus on learning, practicing, and mastering the skill rather than just collecting certificates. The real proof of your expertise is in solving real-world problems, not in the number of certificates you have. That's why I always recommend building data analytics project. Projects help you apply your knowledge, work with real datasets, and tackle challenges similar to what you’d face in a real job. They also showcase your problem-solving skills, creativity, and ability to draw meaningful insights—things no certificate alone can prove. Here, you can find free resources to build your own data portfolio 👇👇 https://t.me/DataPortfolio Like if you agree ❤️ Hope it helps :)

Day 19: Stored Procedures 1. What is a Stored Procedure? A stored procedure is a precompiled set of SQL statements that can be executed with a single call. It helps improve performance, maintainability, and security. 2. Why Use Stored Procedures? ✅ Reduce redundant code. ✅ Improve query performance. ✅ Enhance security by controlling access to direct queries. ✅ Allow parameterized queries for dynamic execution. 3. Syntax for Creating a Stored Procedure CREATE PROCEDURE ProcedureName AS BEGIN -- SQL statements END; Example: A procedure to fetch all employees:

CREATE PROCEDURE GetAllEmployees AS BEGIN SELECT * FROM Employees; END; 
Execute the procedure:
EXEC GetAllEmployees;

4. Stored Procedures with Parameters Stored procedures can take input and output parameters. Example: Procedure to fetch employees based on department ID:

CREATE PROCEDURE GetEmployeesByDept @DeptID INT AS BEGIN SELECT * FROM Employees WHERE DepartmentID = @DeptID; END; 
Execute with a parameter:
EXEC GetEmployeesByDept @DeptID = 2;

5. Stored Procedure with Output Parameters Used to return values from a procedure. Example: A procedure to count employees in a department:

CREATE PROCEDURE GetEmployeeCountByDept @DeptID INT, @EmpCount INT OUTPUT AS BEGIN SELECT @EmpCount = COUNT(*) FROM Employees WHERE DepartmentID = @DeptID; END; 

Call the procedure and get the output value:

DECLARE @Count INT; EXEC GetEmployeeCountByDept @DeptID = 2, @EmpCount = @Count OUTPUT; PRINT @Count; 

6. Modifying and Dropping a Stored Procedure Modify an existing procedure:

ALTER PROCEDURE ProcedureName AS BEGIN 
-- Updated SQL statements END; 
Drop a stored procedure:
DROP PROCEDURE ProcedureName; 

7. Best Practices for Stored Procedures Use meaningful names for easy identification. Avoid SELECT ; instead, specify required columns. Use parameters instead of hardcoded values. Handle errors using TRY...CATCH. Action Steps: Create a stored procedure to insert a new employee into the Employees table. Write a procedure with an input parameter for filtering records. Experiment with an output parameter to return calculated values. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Day 18: Transactions and ACID Properties 1. What are Transactions? A transaction is a sequence of operations performed as a single unit of work. It ensures data consistency, even in cases of failure. 2. Key Characteristics of Transactions: Atomicity: Ensures all operations within the transaction are completed or none at all. Consistency: Guarantees the database remains in a valid state before and after the transaction. Isolation: Transactions are independent and do not interfere with each other. Durability: Once a transaction is committed, the changes are permanent. 3. Syntax for Transactions: Start a Transaction BEGIN TRANSACTION; Commit a Transaction Saves the changes made during the transaction: COMMIT; Rollback a Transaction Reverts the changes made during the transaction: ROLLBACK; 4. Example Without Transactions If an error occurs, only part of the data may be saved, causing inconsistencies:

UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2;

With Transactions Ensures both operations are completed or none:

BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; IF @@ERROR <> 0 ROLLBACK; ELSE COMMIT;

If either UPDATE fails, the entire transaction is rolled back. 5. Savepoints Savepoints allow partial rollback within a transaction:

BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; SAVE TRANSACTION SavePoint1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; ROLLBACK TRANSACTION SavePoint1; -- Reverts the second update only COMMIT;

6. Isolation Levels: Control how transactions interact with each other: Read Uncommitted: Allows dirty reads (data not yet committed). Read Committed: Prevents dirty reads (default in most databases). Repeatable Read: Prevents dirty reads and ensures no changes to data during the transaction. Serializable: Ensures complete isolation but may reduce performance. Set the isolation level:

SET TRANSACTION ISOLATION LEVEL SERIALIZABLE; BEGIN TRANSACTION;

7. Best Practices: Keep transactions short to reduce locking and improve performance. Always use COMMIT or ROLLBACK explicitly. Test for errors within transactions to handle rollbacks. Use appropriate isolation levels based on requirements. Action Steps: Write a transaction to transfer money between two accounts. Experiment with savepoints for partial rollbacks. Explore the effect of different isolation levels on concurrent transactions. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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If you're new to data analytics, start with SQL and Excel. Focus on one at a time, master it, and then move on to the next. Don’t juggle multiple things at once; finish what you start before taking up something new. SQL Topics: SELECT statements WHERE clause GROUP BY and HAVING JOINS (INNER, LEFT, RIGHT, FULL) Aggregation functions (SUM, COUNT, AVG, etc.) Common Table Expressions (CTEs) Subqueries Excel Topics: Basic formulas (SUM, IF, VLOOKUP, etc.) Data cleaning techniques Pivot tables Conditional formatting Charts and graphs Data validation Advanced features like Power Query and Macros SQL Learning Series: https://t.me/sqlspecialist/567 Excel Learning Series: https://t.me/sqlspecialist/664 Hope it helps :)

Day 17: Indexes 1. What are Indexes? Indexes are database objects that speed up the retrieval of data from tables. They function like a book’s index, allowing quick access to specific rows. 2. Types of Indexes Clustered Index: Alters the physical order of table data. Each table can have only one clustered index. Example: Index on a primary key. Non-Clustered Index: Does not change the physical order of data but creates a separate structure for quick lookups. A table can have multiple non-clustered indexes. Unique Index: Ensures that values in a column or group of columns are unique. Composite Index: An index on multiple columns for queries involving those columns. 3. Why Use Indexes? Improve query performance for large datasets. Speed up searches, joins, and filtering. Enforce uniqueness with unique indexes. 4. Syntax for Creating Indexes a) Clustered Index Automatically created when a primary key is defined: CREATE CLUSTERED INDEX IndexName ON TableName (ColumnName); Example:

CREATE CLUSTERED INDEX IDX_EmployeeID ON Employees (EmployeeID);

b) Non-Clustered Index CREATE NONCLUSTERED INDEX IndexName ON TableName (ColumnName); Example:

CREATE NONCLUSTERED INDEX IDX_Salary ON Employees (Salary);

c) Unique Index CREATE UNIQUE INDEX IndexName ON TableName (ColumnName); Example:

CREATE UNIQUE INDEX IDX_UniqueEmail ON Employees (Email);

5. Viewing Indexes To list all indexes on a table: EXEC sp_helpindex 'TableName'; 6. Dropping Indexes To remove an index: DROP INDEX IndexName ON TableName; Example:

DROP INDEX IDX_Salary ON Employees;

7. Limitations of Indexes Slows down INSERT, UPDATE, and DELETE operations due to maintenance. Requires additional storage. Too many indexes can degrade performance. 8. Best Practices: Use indexes for frequently queried columns. Avoid indexing small tables or columns with low selectivity. Regularly monitor and optimize index usage. Action Steps: Create clustered and non-clustered indexes for common queries in your database. Check the performance difference using indexed vs non-indexed columns. Drop unused or redundant indexes. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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5 Essential Portfolio Projects for data analysts 😄👇 1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions. Free websites to find datasets: https://t.me/DataPortfolio/8 2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis. 3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis. 4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner. 5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques. Share with credits: https://t.me/sqlspecialist Like it if you need more posts like this 😄❤️ Hope it helps :)

Day 16: Views and Materialized Views 1. What are Views? A view is a virtual table based on a SELECT query. It does not store data itself but retrieves data from underlying tables when queried. 2. Why Use Views? 1. Simplify complex queries. 2. Enhance security by exposing only specific columns. 3. Maintain consistency across multiple queries. 4. Make queries reusable. 3. Syntax for Creating a View

CREATE VIEW ViewName AS
SELECT Column1, Column2
FROM TableName
WHERE Condition;

Example: Create a view to show employees with a salary greater than 5000:

CREATE VIEW HighSalaryEmployees AS
SELECT EmployeeID, Name, Salary
FROM Employees
WHERE Salary > 5000;

To query the view:

SELECT * FROM HighSalaryEmployees;

4. Updating Data via Views If the view is based on a single table and includes all the necessary primary keys, you can update data through the view:

UPDATE HighSalaryEmployees
SET Salary = 7000
WHERE EmployeeID = 1;

5. Dropping a View To remove a view: DROP VIEW ViewName; 6. Materialized Views A materialized view stores query results physically, unlike regular views. It is refreshed periodically to reflect changes in the underlying data. 7. Why Use Materialized Views? 1. Improve query performance for complex calculations. 2. Reduce load on the database for frequently used queries. 8. Syntax for Creating a Materialized View

CREATE MATERIALIZED VIEW MaterializedViewName
AS
SELECT Column1, Column2
FROM TableName
WHERE Condition;

9. Refreshing Materialized Views Materialized views can be refreshed manually or automatically:

-- Manually refresh
REFRESH MATERIALIZED VIEW MaterializedViewName;

10. Key Differences Between Views and Materialized Views Views: Always fetch data in real-time from the underlying tables. Materialized Views: Store data physically and need refreshing to update. Action Steps 1. Create a view for frequently used queries in your database. 2. Try updating data through a view (if allowed). 3. Experiment with creating and refreshing materialized views. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://t.me/sqlanalyst Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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